Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Pınar Karadayı Ataş

Makale detayları

Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Yazarlar:

Pınar Karadayı Ataş

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Bioinformatic Methods Development | Bioinformatics and Computational Biology (Other) | Genetics (Other) | Biomedical Sciences and Technology | Biomedical Diagnosis

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 4, pp.1261-1274

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306397

pISSN:

2147-3129

Anahtar kelimeler:

Özet:

 Blood disorders are such conditions that impact the bloods ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

 Blood disorders are such conditions that impact the bloods ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

 Blood disorders are such conditions that impact the bloods ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

 Blood disorders are such conditions that impact the bloods ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

Makale detayları

Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Yazarlar:

Pınar Karadayı Ataş

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Bioinformatic Methods Development | Bioinformatics and Computational Biology (Other) | Genetics (Other) | Biomedical Sciences and Technology | Biomedical Diagnosis

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 4, pp.1261-1274

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306397

pISSN:

2147-3129

applied statistic

statistical analysis and application

structural and functional data

machine learning.

Sayı

Makale detayları

Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Yazarlar: Pınar Karadayı Ataş

Yayıncı: Bitlis Eren University

Ana Başlıklar: Artificial Intelligence (Other) | Bioinformatic Methods Development | Bioinformatics and Computational Biology (Other) | Genetics (Other) | Biomedical Sciences and Technology | Biomedical Diagnosis

Dergi: Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 4, pp.1261-1274

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001306397

pISSN: 2147-3129

applied statistic

statistical analysis and application

structural and functional data

machine learning.

Sayı

Yazarlar:

Pınar Karadayı Ataş

Yayıncı:

Bitlis Eren University

Ana Başlıklar:

Artificial Intelligence (Other) | Bioinformatic Methods Development | Bioinformatics and Computational Biology (Other) | Genetics (Other) | Biomedical Sciences and Technology | Biomedical Diagnosis

Dergi:

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi Vol.12 Issue 4, pp.1261-1274

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001306397

pISSN:

2147-3129